CENTER FOR INTERNATIONAL ECONOMICS
Working Paper Series
Working Paper No. 2013-10
The Effect of Corruption on Migration, 1985-2000
Eugen Dimant, Tim Krieger, Daniel Meierrieks
March 2013 The Effect of Corruption on Migration, 1985-2000
This is an Author's Accepted Manuscript of an article published in Applied Economics Letters 20(13), pp. 1270-1274,
[copyright Taylor & Francis] available online at: http://www.tandfonline.com/doi/abs/10.1080/13504851.2013.806776
Eugen Dimant*, Tim Krieger† and Daniel Meierrieks‡
Abstract
We examine the influence of corruption on migration for 111 countries between 1985 and 2000.
Robust evidence indicates that corruption is among the push factors of migration, especially fueling skilled migration. We argue that corruption tends to diminish the returns to education, which is particularly relevant to the better educated.
JEL Classification: D73; F22; O15
Keywords: corruption; migration; skilled migration; push factors of migration
* University of Paderborn, Department of Economics, Warburger Straße 100, 33098 Paderborn,
Germany. Phone: + (49)5251-60-1682. Fax: + (49)5251-60-1679. E-mail:
† Corresponding Author. University of Freiburg, Department of Economics, Wilhelmstraße 1b,
79085 Freiburg, Germany. Phone: + (49)761-203-67651. Fax: + (49)761-203-67649. E-mail: [email protected].
‡ University of Freiburg, Department of Economics, Wilhelmstraße 1b, 79085 Freiburg,
Germany. Phone: + (49)761-203-67653. Fax: + (49)761-203-67649. E-mail: [email protected].
1 1. Introduction
Previous empirical research suggests that, besides socio-economic and demographic factors
(e.g., underdevelopment and demographic pressures), politico-institutional conditions (e.g., political instability) are among the push factors of international migration (cf., e.g., Hatton and
Williamson, 2003; Mayda, 2010; Dreher et al., 2011; Docquier and Rapoport, 2012). We argue that a related important push factor, which has so far been mostly neglected in both theoretical and empirical research, is corruption.
Corruption is associated with a number of unfavorable outcomes. For one, corruption tends to negatively affect a country’s (short-run) level of economic activity.1 Economies plagued by high levels of corruption grow more slowly, e.g., as corruption contributes to an inefficient allocation of resources (Jain, 2001; Campos et al., 2010). For another, corruption may also worsen a country’s (structural) socio-economic situation. For instance, Gupta et al.
(2002) find that high levels of corruption promote income inequality and the spread of poverty.
Furthermore, corruption may also lead to suboptimal patterns of social mobility when it matters more strongly to upward mobility than actual merits.
In sum, the prevalence of corruption is likely to worsen individual working and living conditions for the majority of citizens. It may therefore also matter to the calculus of a potential migrant. Individual education is a particularly important factor influencing migration decisions.
Here, corruption tends to lower the returns to education, e.g., by contributing to slow economic growth and unemployment, widespread inequality and the lack of social advancement. Given the irreversibility of human capital investment, corruption may make it more attractive to migrate to recoup one’s individual education investment. Here, we expect the highly skilled to
1 Note, however, that this assessment does not rule out that corruption may actually yield positive economic effects under specific circumstances (Dreher and Gassebner, 2013).
3 be particularly responsive to the prevalence of corruption, given their high level of human capital investment and subsequent need for particularly high (i.e., cost-effective) skill premiums.2 Based on these lines of reasoning, we hypothesize that corruption is among the push factors of migration and particularly relevant to skilled migration.
2. Data and methodology
To empirically examine this hypothesis, we compile data on (skilled and average) migration, corruption and further controls for 111 countries between 1985 and 2000. The summary statistics and the operationalization of the controls are reported in Table 1.3 Data on our dependent variables, the migration rates, are drawn from Defoort (2008) who provides estimates of the rates of skilled and average migration to six main receiving countries
(Australia, Canada, France, Germany, the U.K. and the U.S.). Here, the skilled migration rate refers to the ratio of the number of skilled emigrants (who exhibit a post-secondary certificate) to the total number of skilled natives aged 25 or older, while the average migration rate is defined as the ratio of the total number of emigrants to the total number of natives aged 25 or older (Defoort, 2008).
2 In addition to that, the increase of income inequality and poverty caused by corruption (Gupta et al., 2002) may foster the political demand for redistribution. As the better skilled are typically the typical net payers of (progressive) income taxes, this may further fuel skilled emigration.
3 The migration data is available only for three points in time (1990, 1995 and 2000). Therefore, we use five-year averages of the explanatory variables (for the 1986-1990, 1991-1995 and
1996-2000 periods) to estimate their influence on migration.
4
5 Corruption data are drawn from the International Country Risk Guide (ICRG, 2009).4
Surveying experts, the ICRG issues a corruption index that we use as our main explanatory variable. Here, corruption refers to financial corruption associated with conducting business
(e.g., bribes) as well as other forms of political corruption such as excessive patronage, nepotism and close ties between politics and business. Note that we rescaled the ICRG corruption index, so that higher values correspond to higher corruption levels.
To avoid detecting only spurious effects of corruption on migration, we also consider a number of confounding controls that may simultaneously affect corruption and migration. For instance, we control for the effect of economic development, as richer countries are both less susceptible to corruption and less likely to have high migration rates. Following the empirical literature on the determinants of corruption (Serra, 2006) and migration (Docquier and
Rapoport, 2012), in our baseline specification we control for the effect of per capita income, population size, regime type and political instability. As robustness checks, we amend this model with additional controls for demographic pressure (youth burden), institutional quality
(the quality of a country’s bureaucracy), trade openness and certain country-specific traits that may affect migration costs (colonial ties and distance between sending and destination countries).5
4 We use the ICRG data because it is available since 1984, making a panel estimation approach to the corruption-migration nexus possible. Other corruption measures are available only for shorter time periods. Jain (2001: 77) notes that the various corruption measures are usually highly correlated.
5 Our findings are also robust to the inclusion of further controls for religious fractionalization, oil production, government size, further geographic and historic country characteristics
(landlocked location, common language) and education (years of schooling).
6 Initial tests indicate the presence of autocorrelation, heteroskedasticity and cross- sectional dependence, as it is common for panel data with country-year observations. We therefore run a series of pooled OLS and fixed-effects regressions with Driscoll-Kraay standard errors that are robust to these data characteristics (Driscoll and Kraay, 1998).6
3. Empirical results
The pooled OLS estimates are reported in Table 2. We find that corruption has a positive and statistically significant effect on both skilled and average migration. However, the marginal effect of corruption on skilled migration tends to be approximately three to four times higher than its effect on average migration. This finding provides first support for our hypothesis that corruption as among the push factors of migration and especially matters to the migration decisions of the highly skilled.
The fixed-effects estimates—which truly consider the panel structure of our dataset— are reported in Table 3.7 We find that corruption only has a positive, statistically significant and specification-robust effect on skilled migration, but has no significant impact on average migration. This result further strengthens our previous finding that the decision of the highly skilled to emigrate is strongly affected by the disincentive of corruption at home.
6 We also experimented with instrumental variable (IV) estimations, as reverse causation may be an issue. However, pooled and fixed-effects IV-estimations (where corruption is instrumented by the quality of judicial institutions and the degree of democratic participation) do not indicate that corruption is endogenous to migration. Also, Durbin-Wu-Hausman tests suggests that any endogeneity among the regressors does not bias our estimates.
7 Note that all constant influencing factors (colonial ties, distance etc.) are now subsumed under the fixed effects.
7
8
9 Finally, note that the results for the control variables are largely in line with previous research.
For instance, both the pooled OLS and fixed-effects estimates suggest that skilled migration is less common in richer countries, as previously reported in Docquier and Rapoport (2012). As another example, the positive effect of political instability on skilled migration in the fixed- effects regressions is in line with Dreher et al. (2011).
4. Conclusion
We examine the impact of corruption on migration for a panel of 111 countries between 1985 and 2000. Our empirical results indicate that corruption especially drives skilled migration, while its effect on average migration is less pronounced and not statistically robust. Our main finding is consistent with the hypothesis that corruption lowers the returns to education and consequently matters most to the calculus of (prospective) highly skilled migrants. Corruption control may therefore be an important policy tool to rein the brain drain, particularly when this brain drain is associated with predominantly poor development outcomes.
10 References
Campos, N.F., Dimova, R., Saleh, A., 2010. Whither corruption? A quantitative survey of the
literature on corruption and growth. CEPR Discussion Paper Series 8140.
Defoort, C., 2008. Long-term trends in international migration: An analysis of the six main
receiving countries. Population 63, 285-317.
Docquier, F., Rapoport, H., 2012. Globalization, brain drain, and development. Journal of
Economic Literature 50, 681-730.
Dreher, A., Gassebner, M., 2013. Greasing the wheels? The impact of regulations and
corruption on firm entry. Public Choice, in press.
Dreher, A., Krieger, T., Meierrieks, D., 2011. Hit and (they will) run: The impact of terrorism
on migration. Economic Letters 113, 42-46.
Driscoll, J.C., Kraay, A.C., 1998. Consistent covariance matrix estimation with spatially
dependent panel data. Review of Economics and Statistics 80, 549-560.
Gupta, S., Davoodi, H., Alonso-Terme, R., 2002. Does corruption affect income inequality and
poverty? Economics of Governance 3, 23-45.
Hatton, T.J., Williamson, J.G., 2003. Demographic and economic pressure on emigration out
of Africa. Scandinavian Journal of Economics 105, 465-485.
Jain, A.K., 2001. Corruption: A review. Journal of Economic Surveys 15, 71-121.
Mayda, A.M., 2010. International migration: A panel data analysis of the determinants of
bilateral flows. Journal of Population Economics 23, 1249–1274.
Serra, D., 2006. Empirical determinants of corruption: A sensitivity analysis. Public Choice
126, 225-256.
11 Appendix
Albania Dom. Republic Italy Papua N. Guinea Tunisia Algeria Ecuador Jamaica Paraguay Turkey Angola Egypt Japan Peru Uganda Argentina El Salvador Jordan Philippines U. Arab Emirates Australia Ethiopia Kenya Poland United Kingdom Austria Finland Kuwait Portugal United States Bahrain France Liberia Qatar Uruguay Bangladesh Gabon Libya Romania Venezuela Belgium Gambia Madagascar Saudi Arabia Vietnam Bolivia Germany Malawi Senegal Zambia Botswana Ghana Malaysia Sierra Leone Zimbabwe Brazil Greece Mali Singapore Bulgaria Guatemala Mexico Somalia Burkina Faso Guinea Mongolia South Africa Cameroon Guinea-Bissau Morocco South Korea Canada Guyana Mozambique Spain Chile Haiti Netherlands Sri Lanka China Honduras New Zealand Sudan Colombia Hungary Nicaragua Sweden Congo (Republic) India Niger Switzerland Costa Rica Indonesia Nigeria Syria Cote d’Ivoire Iran Norway Tanzania Cuba Iraq Oman Thailand Cyprus Ireland Pakistan Togo Denmark Israel Panama Trinidad
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